1. Field of the Invention
The present invention relates to a method for estimating the contour of a video object. More particularly, the present invention relates to video compression and transmission technologies and their ability or lack thereof to distinguish the video object from a video background during video communications or a video call.
2. Description of the Related Art
The development of video compression and transmission technologies in rennet years has made video communications and/or video calls popular. Yet, there has been little progress in technically overcoming many of the adverse effects relating to the video communications.
One major adverse effect of video communications is that the place where a user is located is shown and thus his privacy is impaired because one receiving the communication can see a detailed background of the area surrounding the participant, such as their home or office area. To avert this problem and render the video communications active, a technique for distinguishing an object from a background in video communications and processing them separately is required. As the background can be replaced by another background, the place of the user is not known.
Many techniques have been proposed to date for distinguishing a video object from a video background. A popular technique is background modeling. When a fixed camera is used, pixels constant for a predetermined time are set as a background, as a user would be extremely likely to move, or at the very least, change facial expressions and speak, which would change the pixel values. Pixels whose values are rapidly changed with respect to the background are considered to be an object. Since the background may vary in some cases, it is updated every predetermined time, thus reducing sensitivity to changes.
Based on the assumption that in a video conference/communication the successive frames of the background are typically similar, the background modeling scheme effectively models the background and enables fast identification of the object using the modeled background. Thus, the background modeling considers all changes other than the background as the object, which is typically a person. Background modeling based on a Gaussian mixture model or kernel density estimation updates the background adaptively to instantaneous or continuous changes.
However, the above conventional background modeling scheme requires a fixed camera because movement of the camera would cause a shifted view that would make cause pixel changes in the background. Because modeling is carried out for each pixel in temporally adjacent frames, a change in pixel position causes errors. Even when the camera is displaced by one pixel, wrong information is mistaken for an object. In addition, the computation and storage capacities of the device may affect the modeling result considerably. Due to these limitations, the conventional technology is limited to a surveillance camera and computer-aided video communications. Accordingly, there exists a long-felt need in the art for an object segmentation method applicable to a future-generation communication technology, portable terminal-based video communications.